In the last decade, genome-wide association studies (GWAS), using mainly microarrays, and high throughput sequencing, have become common tools in biomedical and biological research. Most of these tests are conducted to identify genes differentially expressed in a specific disease compared to the healthy state.

However, the potential of the et of data generated from such next-generation technology approaches goes beyond the initial hypothesis. Making available these information (differential gene expression, methylation analysis or association of polymorphisms with a partycular disease) by web tools, cuold help other researchers improving the selection of candidate genes or give priority to the study of new genes.

The tool to be developed in this proposal would take a step forward drom those described above. The project will include the selection of robust biomarkers based on the exploitation of data from references and functional genomic experiments (transcriptional or genomic and epigenomic). The experimental conditions chosen by the user will be also taken into account. Indeed, standard criteria such as accuracy, reliability and usefulness will be also provided to validate a mining model data, decreasing the probability of fdatabaseinding associations due to cross correlation data. 

This project aims to develop an easy to use-fast bioinformatic tool, useful to identify biomarkers associated with the development or risk of a specific syndrome. A PhD has been hired to go ahead with this project through the Torres Quevedo fellowship. Funding for this bioinformatic tool development is being sought.
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